Big Data and Semantic Web in Manufacturing

Big Data and Semantic Web in Manufacturing

Nitesh Khilwani, PhD

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Outline

• Big data in Manufacturing• Big data Analytics• Semantic web technologies• Case study:

– 1. ECOS Ontology– 2. Text2Rdf– 3. Semantic Layer for Information management.

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Manufacturing

• Gone are the days when a manufacturing companies operated autonomously

• Using advanced IT systems companies are linking with consumer, manufacturing operations and suppliers to work as single entity.

MINING/FARMING SUPPLY CHAIN MANUFACTURING DISTRIBUTION CUSTOMER

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Manufacturing

• Lots of data is being collected and warehoused – purchases at department/

grocery stores– Bank/Credit Card

transactions– Social Network– Web data, e-commerce

• Big data: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools.

MANUFACTURING

Business

Technology Workforce

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Big Data in Manufacturing

• Big Data in manufacturing has the potential to revolutionize how we build, produce and live.

• For decades, manufacturers have invested in and developed technologies that leverage “just in time” and “Six Sigma” methodologies.

• Next step is data analytics...

Big Data:Decisions based on

all your data

Video and Images

Machine-Generated DataSocial Data

Documents

http://en.wikipedia.org/wiki/Just_in_time_(business)

https://en.wikipedia.org/wiki/Six_Sigma

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Big data Use cases

Today’s Challenge New Data What’s Possible

ManufacturingInter department Support Product sensors Automated diagnosis, support

HealthcareExpensive office visits Remote patient monitoring Preventive care, reduced

hospitalization

Location-Based ServicesBased on home zip code Real time location data Geo-advertising, traffic, local

search

Public SectorStandardized services Citizen surveys Tailored services,

cost reductions

RetailOne size fits all marketing Social media Sentiment analysis

segmentation

Big Data Is About…

Tapping into diverse data sets

Finding and monetizingunknown relationships

Data driven business decisions

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Big Data Landscape

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Tools for Big data Analytics

• Visualization: baseline tools for both experienced data scientists and more novice analysts to make sense of data.

• Data mining: To make machines more capable to automate the analysis.

• Predictive Analysis: Tools to look forward and analyse the problems

• Semantic engine: Tools to parse, extract, and analyze data

• Data quality and Master Management: To insure quality and management process around data.

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Gartner Life Cycle of Emerging Technologies -2013

• Big data and supporting technologies are part of Hype Cycle

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Big data in Manufacturing: Benefits and Challenges

Benefits

Product quality and defect trackingSupply chain management

Forecasting of manufacturing output

Increase efficiency

Simulation and testing of new processes

Enabling customization in manufacturing

Challenges

Building trust between data scientist and managers.Confidence to handle large volume, velocity and variety of dataFinding the door for Big data to enter our current system.Determining which technology to useDetermining what to do with the analysis from Big dataMaintaining the consistency for using big data.

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Information floating in Manufacturing

Bug Tracking and Management

(Bugzilla)

Version Control (SVN, Perforce)

Knowledge Management System (Wiki)

• Issue• Project• Component• Release• Status• Tester• Developer

• Component• Code• Version• Issue• Developer• Activity

• Project• Requirement• Milestones• Test plan• Process• Author

Competency Management

(Website)

• Hierarchy• Organizational • Structure• Roles• Responsibility• Developer• Tester

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(Bugzilla)







(Website)


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(Bugzilla)







(Website)


• Interval between bug fixing time and change commit time

• Mapping between bug owner and change committer

• Similarity between bug report and change logs

• Alternate contact points for emergency situations.

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Architecture for Data Analysis

CRM

Applications

APPLICATION FRAMEWORK

Authentication, Authorization, Query transformation, Personalization,

display

Applications Applications

DATA CONNECTION

LAYER

APPLICATION LAYER

USER MANAGEMENT

LAYER

PROCESSING LAYER

SEARCH ENGINE

IndexingCrawling

Converting

TEXT ANALYTICS

ThesaurusClustering

Semantic ProcessingEntity Extraction

SEMANTICS

MetadataOntologyTagging

Semantic Web

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Semantic Web

• Semantic web is an extension of current web technology in which information is annexed with well defined meaning– Provides machine processable

semantics of data

• Ontology: Basic building block for Semantic Web– Captures semantics and relations of

a domain

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Semantic Web Technologies

• RDF: Language for representing knowledge

• RDFS: Vocabulary for defining classes and properties

• OWL: Adding relations in RDFS• SPARQL: Query language based

on graphs.• Rules: Language to combine

different ontology.

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Ontology: ECOS

• ECOS Ontology: Enterprise Competence Organization Schema

– Publishing enterprise competence in an explicit and structured manner

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ECOS

• Use other ontologies to extend the ECOS.• Standards defined by UN and EU are used in ECOS for representing

competences

General

Specific

Enterprise RecordBusiness

Company Name Contact PersonAddress Key Person

Past Projects

Relation

Resource Process Unit Skill

Product/Service

Customer

Sector

Preference

Financial

Summary

Plan

Achievement

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TEXT2RDF

TEXT2RDF

Text mining approach for providing explicit semantic description to the

documents.

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Semantic Information in Manufacturing


(Bugzilla)







(Website)


METADATA METADATA METADATA METADATA

SEMANTIC LAYER

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Semantics in Enterprise

• Supply Chain Management—Biogen Idec• Media Management—BBC• Data Integration in Oil & Gas—Chevron• Web Search and Ecommerce

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Data & Analytics

Big Data and Semantic Web in Manufacturing